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Economics 883 Tim Bollerslev Duke University Boller@Duke.Edu Spring 2015 919-660-1846 Economics 883 Tim Bollerslev Duke University [email protected] Spring 2015 919-660-1846 Econometrics for Financial and Macroeconomic Time Series Overview: The specification, estimation, diagnostic testing, and practical usage of dynamic models for economic and financial time series present a host of unique challenges, requiring the use of specialized statistical models and inference procedures. This course provides a selective overview of some of the most important of these approaches. The discussion will focus on the practical implementation of the different techniques, rather than formal proofs, including applications in both macroeconomics and asset pricing finance. Requirements: I will assume that you have an understanding of econometrics and basic statistics at the level of first-year graduate econometrics, equivalent to Econ.703D and Econ.707D at Duke. Class Schedule: Lectures will be held in Room 111, Tuesdays and Thursdays, 1:25-2:40pm. Office Hours: My office hours are Wednesdays, 2:00-3:00pm in Room 313. Webpage: http://www.econ.duke.edu/~boller/Econ.883 Evaluation: Your grade for the course will be based on an equal weighting of your performance on the final exam and four problem sets. Then final exam is scheduled for Monday, April 27, 9:00-11:00am. You are encouraged to work on the problem sets in groups of up to four people. If you do work in a group, each group should hand in only one solution to the assignment. I may also consider your participation in the classroom discussions when determining your final grade for the course. Books: The main textbook for the course is: James D. Hamilton (1994). Time Series Analysis. Princeton, NJ: Princeton University Press. This is a classic. It provides an exceptionally detailed and comprehensive discussion of the most important ideas in time series econometrics as of ~twenty years ago. Some of the discussion is a bit dated by now. It is a great general reference book, however. In addition you might want to look at the more recent book: Vance Martin, Stan Hurn and David Harris (2013). Econometric Modelling with Time Series. Cambridge University Press. This book offers a systematic framework for the specification, testing, and estimation of time series models. It strikes an excellent balance between formal theory, intuition, and actual empirical applications, with an emphasis on maximum likelihood techniques. Parts of the book nicely complements many of my lectures. It also comes with a very comprehensive set of GAUSS, MATLAB and R routines. Other classic and recent books pertaining to the statistical and econometric analysis of economic and financial time series include: Theodore W. Anderson (1971). The Statistical Analysis of Time Series. John Wiley & Sons, Inc. (A classic that laid the foundation for many of the subsequent theoretical developments.) George E.P. Box and Gwilym M. Jenkins (1969). Time Series: Forecasting and Control. Holden-Day Inc. (The classical book on time series analysis that really started the field.) Peter J. Brockwell and Richard A. Davis (2006). Time Series: Theory and Methods, 2nd Ed. Springer-Verlag. (An excellent more rigorous introduction to traditional time series analysis.) Walter Enders (2009). Applied Econometric Time Series, Second Edition, 3rd Ed. John Wiley & Sons, Inc. (An intuitive applications oriented general discussion of time series econometrics.) Christian Gourieroux and Joann Jasiak (2001). Financial Econometrics. Princeton University Press. (The first part of this book contains a good all-around survey of time series econometrics.) Andrew C. Harvey (1990). Econometric Analysis of Time Series, 2nd Ed. MIT Press. (Although this was first published more than two decades ago, it remains a good reference for many of the basic topics.) Eric Jondeau, Ser-Huang Poon and Michael Rockinger (2007). Financial Modeling Under Non- Gaussian Distributions. Springer-Verlag. (A nice up-to-date discussion of applied financial time series econometrics.) Maurice B. Priestley (1981). Spectral Analysis and Time Series. Academic Press. (The classical textbook treatment of spectral analysis.) Stephen J. Taylor (2005). Asset Price Dynamics, Volatility, and Prediction. Princeton University Press. (A very nice applications oriented summary of different time series procedures and techniques, with an emphasis on uses in empirical finance and volatility modeling.) Ruey S. Tsay (2013). An Introduction to Analysis of Financial Date with R. John Wiley & Sons, Inc. (An easy-to-read introduction to the analysis of financial time series.) - 2 - Course Outline and Readings: In addition to the relevant chapters in the book by Hamilton, we will also discuss several journal articles and Handbook chapters. In general, however, I will mostly rely on my own notes and interpretation. 1. Univariate ARMA Models Hamilton, Chapters 3, 4. Hamilton, Chapters 1, 2 (this is review material about difference equations and lag operators). Martin, Hurn and Harris, Chapter 13. Anderson, Chapters 5-7. Box and Jenkins, Chapters 1-9. Brockwell and Davis, Chapters 1, 3, 5, 7, and 9. Enders, Chapters 1, 2. Taylor, Chapter 3. Tsay, Chapters 2-3. 2. MLE, QMLE and Estimation-by-Simulation Hamilton, Chapter 5. Martin, Hurn and Harris, Chapters 1-2, 7, 9 and 12. Tim Bollerslev and Jeffrey M. Wooldridge (1992), "Quasi-Maximum Likelihood Estimation and Inference in Dynamic Models with Time Varying Covariances," Econometric Reviews, 11, 143-172. George Tauchen and A. Ronald Gallant (1996), "Which Moments to Match?," Econometric Theory, 12, 657-681. Brockwell and Davis, Chapter 8. Harvey, Chapters 3-4. 3. Hypothesis Testing and Model Selection Hamilton, Chapter 5. Martin, Hurn and Harris, Chapter 4. - 3 - Robert F. Engle (1986), "Wald, Likelihood Ratio, and Lagrange Multiplier Tests in Econometrics," Chapter 13 in Handbook of Econometrics, Vol.2, (Zvi Griliches and Michael D. Intriligator, eds.). Amsterdam: Elsevier Science B.V. John Geweke and Richard Meese (1981), "Estimating Regression Models of Finite but Unknown Order," International Economic Review, 22, 55-70. 4. Spectral Analysis and Filtering Hamilton, Chapter 6 and Sections 10.4-10.5. Marianne Baxter and Robert G. King (1999), "Measuring Business Cycles: Approximate Band-Pass Filters for Economic Time Series," Review of Economics and Statistics, 81, 575-593. Robert F. Engle (1976), "Interpreting Spectral Analysis in Terms of Time Domain Models," Annals of Economic and Social Measurement, 5, 89-109. Tim Bollerslev, Daniela Osterrieder, Natalia Sizova, and George Tauchen (2013), "Risk and Return: Long-Run Relationships, Fractional Cointegration, and Return Predictability," Journal of Financial Economics, 108, 409-424. Anderson, Chapters 8-9. Brockwell and Davis, Chapters 4, 10, and Sections 11.6-11.8. Priestley, Chapters 1, 4-11. 5. Vector Autoregressions Hamilton, Sections 10.1-10.3 and Chapter 11. Martin, Hurn and Harris, Chapters 14. James H. Stock and Mark W. Watson (2001), "Vector Autoregressions," Journal of Economic Perspectives, 15, 101-115. Mark W. Watson (1994), "Vector Autoregressions and Cointegration," Section 4, Chapter 47 in Handbook of Econometrics, Vol.4, (Robert F. Engle and Daniel McFadden, eds.). Amsterdam: Elsevier Science B.V. Enders, Chapter 5. Gourieroux and Jasiak, Chapters 3, 4. - 4 - 6. GMM Hamilton, Chapter 14. Martin, Hurn and Harris, Chapter 10. Lars P. Hansen and Kenneth J. Singleton (1982), "Generalized Instrumental Variables Estimation of Non-Linear Rational Expectations Models," Econometrica, 50, 1269-86, "Errata," Econometrica, 52, 267-268. Ulrich K. Müller (2014), "HAC Corrections for Strongly Autocorrelated Time Series," Journal of Business and Economic Statistics, 32, 311-321. 7. Unit Roots Hamilton, Chapters 15-18. Martin, Hurn and Harris, Chapters 16-17. Hamilton Chapter 7 (contains review material on standard asymptotic distribution theory for stationary processes). James H. Stock (1994), "Unit Roots, Structural Breaks and Trends," Chapter 46 in Handbook of Econometrics, Vol.4, (Robert F. Engle and Daniel McFadden, eds.). Amsterdam: Elsevier Science B.V. 8. Cointegration Hamilton, Chapters 19-20. Martin, Hurn and Harris, Chapter 18. "Time-Series Econometrics: Cointegration and Autoregressive Conditional Heteroskedasticity," Advanced Information on the 2003 Nobel Prize in Economic Sciences. Clive W.J. Granger and Paul Newbold (1974), "Spurious Regressions in Econometrics," Journal of Econometrics, 2, 111-120. Phillips, P.C.B. (1986), "Understanding Spurious Regressions in Econometrics," Journal of Econometrics, 33, 311-340. Mark W. Watson (1994), "Vector Autoregressions and Cointegration," Section 3, Chapter 47 in Handbook of Econometrics, Vol.4, (Robert F. Engle and Daniel McFadden, eds.). Amsterdam: Elsevier Science B.V. - 5 - 9. Long-Memory and Fractional Differencing Richard T. Baillie (1996), "Long-Memory Processes and Fractional Integration in Econometrics," Journal of Econometrics 73, 5-59. Richard T. Baillie, Tim Bollerslev and Hans O. Mikkelsen (1996), "Fractionally Integrated Generalized Autoregressive Conditional Heteroskedasticity," Journal of Econometrics, 74, 3-30. Tsay, W. J., and C.-F. Chung (2000), "The Spurious Regression of Fractionally Integrated Processes," Journal of Econometrics, 96, 155-182. 10. Volatility Hamilton, Chapter 21. Martin, Hurn and Harris, Chapter 20. Torben G. Andersen, Tim Bollerslev, Peter Christoffersen and Francis X. Diebold (2013), "Financial Risk Measurement for Financial Risk Management," in Handbook of the Economics of Finance (George Constantinides, Milton
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